ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1492261
Research on the evolutionary path of intelligent energy technology based on Subject-Action-Object structure and Latent Dirichlet Allocation
Provisionally accepted
- China University of Mining and Technology, Xuzhou, China
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Analyzing the evolutionary path of intelligent energy technology (IET) is important, which helps to forecast the future directions of research and development (R&D) for enterprises and make policy priority for governments. While some scholars have studied intelligent energy patents, few have explored the evolutionary path of this technology. This study aims to fill this gap by describing the evolutionary path and future trends of IET. Using methods like the Girvan-Newman algorithm, Latent Dirichlet Allocation, Search Path Count algorithm, and Subject-Action-Object (SAO) structure, it seeks to provide information support for government to optimize industrial decision-making and enterprises to determine the direction of and investment. The novelties of this study lie in taking the evolutionary path of IET as the research object, and integrating the technology evolutionary path recognition method based on SAO and Main Path Analysis (MPA) to improve accuracy and efficiency, and visualizing the research results to predict the future expansion of the technology evolution path. Meanwhile, Sentence-BERT model is used to extract semantically rich sentence vectors address sparse semantic features in patent abstracts. This research contributes by uncovering evolutionary paths in IET across three key domains (Intelligent Energy Systems, Intelligent Buildings, and Modular Electric Communication), while providing strategic guidance for enterprise R&D and policy-making to enhance competitiveness and drive sustainable industrial growth.
Keywords: Intelligent energy technology, Evolutionary path, Main path analysis, Subject-Action-Object structure, Sentence-BERT
Received: 03 Jan 2025; Accepted: 12 May 2025.
Copyright: © 2025 Liu, Xi and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chao Xu, China University of Mining and Technology, Xuzhou, China
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